Vehicle Detection Project¶
The goals / steps of this project are the following:
- Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier -- COMPLETED
- Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector. -- COMPLETED
- Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing. -- COMPLETED
- Implement a sliding-window technique and use your trained classifier to search for vehicles in images. -- COMPLETED
- Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles. -- COMPLETED
- Estimate a bounding box for vehicles detected. -- COMPLETED
Load the images¶
In [1]:
import os
import glob
# It turns out the zip file extracted to mutliple directories includes GTI and KTTI. Use the method from
# the tutorial video to read from multiple directory
# Build up a directory list for cars
basedir = 'vehicles/'
image_types = os.listdir(basedir)
cars = []
for imtype in image_types:
cars.extend(glob.glob(basedir+imtype+'/*'))
print('Number of Vehicle Image Found: ', len(cars))
# save the number in a txt file for later use
with open('car.txt', 'w') as f:
for fn in cars:
f.write(fn+'\n')
basedir = 'non-vehicles/'
image_types = os.listdir(basedir)
notcars = []
for imtype in image_types:
notcars.extend(glob.glob(basedir+imtype+'/*'))
print('Number of Non-Vehicle Image Found: ', len(notcars))
# save the number in a txt file for later use
with open('notcar.txt', 'w') as f:
for fn in notcars:
f.write(fn+'\n')
Import the library¶
In [2]:
import matplotlib.image as mpimg
import numpy as np
import cv2
from skimage.feature import hog
import matplotlib.pyplot as plt
import cv2
import time
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
Function Library from the lessons¶
In [3]:
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
# Define a function to compute binned color features
def bin_spatial(img, size=(32, 32)):
# Use cv2.resize().ravel() to create the feature vector
#features = cv2.resize(img, size).ravel()
# Return the feature vector
# create a features with 3 color
color1 = cv2.resize(img[:,:,0], size).ravel()
color2 = cv2.resize(img[:,:,1], size).ravel()
color3 = cv2.resize(img[:,:,2], size).ravel()
return np.hstack((color1, color2, color3))
# Define a function to compute color histogram features
# NEED TO CHANGE bins_range if reading .png files with mpimg!
# remove bins_range=(0, 256) which has no effect
#def color_hist(img, nbins=32, bins_range=(0, 256)):
def color_hist(img, nbins=32):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins)
channel2_hist = np.histogram(img[:,:,1], bins=nbins)
channel3_hist = np.histogram(img[:,:,2], bins=nbins)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
file_features = []
# Read in each one by one
image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
file_features.append(hog_features)
features.append(np.concatenate(file_features))
# Return list of feature vectors
return features
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True, vis=False):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
if hog_feat == True:
if vis==False:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
else:
if hog_channel == 'ALL':
hog_features = []
# since there are multiple hog_image for each channel, we are just showing the hot image for last channel
for channel in range(feature_image.shape[2]):
hog_feature, hog_image = get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=True, feature_vec=True)
hog_features.extend(hog_feature)
else:
hog_features, hog_image = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=True, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#9) Return concatenated array of features
if vis==False:
return np.concatenate(img_features)
else:
return np.concatenate(img_features), hog_image
# Define a function you will pass an image
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=9,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat, vis=False)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
#6) Predict using your classifier
prediction = clf.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
# Define a function for plotting
def visualize(fig, rows, cols, imgs, titles):
for i, img in enumerate(imgs):
plt.subplot(rows, cols, i+1)
plt.title(i+1)
img_dims = len(img.shape)
if img_dims < 3:
plt.imshow(img, cmap='hot')
plt.title(titles[i])
else:
plt.imshow(img)
plt.title(titles[i])
In [4]:
%matplotlib inline
## Test the function library and turn the parameter
## get a random image fr car and non-car directory
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
### TODO: Tweak these parameters and see how the results change.
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 16 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
y_start_stop = [None, None] # Min and max in y to search in slide_window()
car_features, car_hog_image = single_img_features(car_image, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat, vis=True)
notcar_features, notcar_hog_image = single_img_features(notcar_image, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat, vis=True)
images = [car_image, car_hog_image, notcar_image, notcar_hog_image]
titles = ['car image', 'car hog image', 'not car image', 'not car hog image']
fig = plt.figure(figsize=(12,3))
visualize(fig, 1, 4, images, titles)
Use SVC Linear to train the classifier¶
In [5]:
### Parameter turning for best SVC linear training result
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 16 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
y_start_stop = [None, None] # Min and max in y to search in slide_window()
# Use the lesson video example to set a smaller dataset for training.
t=time.time()
n_samples = 1000
random_idxs = np.random.randint(0, len(cars), n_samples)
# Use up to n_samples of data set
#test_cars = np.array(cars)[random_idxs]
#test_notcars = np.array(notcars)[random_idxs]
# Use all data
test_cars = cars
test_notcars = notcars
car_features = extract_features(test_cars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(test_notcars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
print(time.time() - t, ' seconds to complete features')
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.2, random_state=rand_state)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
Test with sample images from the test_images directory¶
In [6]:
example_images = glob.glob('test_images/*')
images = []
titles = []
# 400 to start 400 + 96 x 3 = 688 to stop
y_start_stop = [400, 688] # Min and max in y to search in slide_window()
overlap = 0.5
for img_src in example_images:
t1 = time.time()
image = mpimg.imread(img_src)
draw_image = np.copy(image)
# Uncomment the following line if you extracted training
# data from .png images (scaled 0 to 1 by mpimg) and the
# image you are searching is a .jpg (scaled 0 to 255)
image = image.astype(np.float32)/255
print(np.min(image), np.max(image))
# experience 64x64 and 128x128, 96x96 get the best result.
windows = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop,
xy_window=(96, 96), xy_overlap=(overlap, overlap))
hot_windows = search_windows(image, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 255), thick=6)
images.append(window_img)
titles.append('')
print(time.time() - t1, ' second to preocess a single image with ', len(windows), ' windows')
fig = plt.figure(figsize=(12,18), dpi=300)
visualize(fig, 3, 2, images, titles)
find_car function for hog-sub-sampling¶
In [7]:
def convert_color(img, conv='RGB2YCrCb'):
if conv == 'RGB2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
if conv == 'BGR2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
if conv == 'RGB2LUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
draw_img = np.copy(img)
heatmap = np.zeros_like(image[:,:,0]).astype(np.float)
img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
#test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
heatmap[ytop_draw+ystart:ytop_draw+win_draw+ystart, xbox_left:xbox_left+win_draw] += 1
return draw_img, heatmap
In [8]:
# Test out the find_car function
ystart = 400
ystop = 688
scale = 1.5
images= []
titles= []
for img_src in example_images:
t1 = time.time()
image = mpimg.imread(img_src)
draw_img1, heatmap1 = find_cars(image, 400, 688, 1.5, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
draw_img2, heatmap2 = find_cars(image, 400, 500, 0.8, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
heatmap = heatmap1 + heatmap2
images.extend((draw_img2, heatmap))
titles.extend(('', ''))
print(time.time() - t1, ' second to preocess a single image with ', len(windows), ' windows')
fig = plt.figure(figsize=(12,18), dpi=300)
visualize(fig, 6, 2, images, titles)
apply threshold for heat map and draw_labeled boxes¶
In [9]:
from scipy.ndimage.measurements import label
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
In [10]:
# Test the find_car, apply_threshold and draw_labeled_boxes
# Test out the find_car function
ystart1 = 400
ystop1 = 500
scale1 = 0.8
ystart2 = 400
ystop2 = 688
scale2 = 1.5
images= []
titles= []
for img_src in example_images:
t1 = time.time()
image = mpimg.imread(img_src)
out_img1, heatmap1 = find_cars(image, ystart1, ystop1, scale1, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
out_img2, heatmap2 = find_cars(image, ystart2, ystop2, scale2, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
heatmap = heatmap1 + heatmap2
heatmap = apply_threshold(heatmap, 2)
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(image), labels)
images.extend((draw_img, heatmap))
titles.extend(('', ''))
print(time.time() - t1, ' second to preocess a single image with ', len(windows), ' windows')
fig = plt.figure(figsize=(12,18), dpi=300)
visualize(fig, 6, 2, images, titles)
create a image pipeline¶
In [17]:
from collections import deque
def pipeline(img):
ystart1 = 400
ystop1 = 500
scale1 = 0.8
ystart2 = 400
ystop2 = 688
scale2 = 1.5
# applied mutliple search area with differnet search windows. 64 x 0.8 for new horizon, then 64 x 1.5 for lower half
out_img1, heatmap1 = find_cars(img, ystart1, ystop1, scale1, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
out_img2, heatmap2 = find_cars(img, ystart2, ystop2, scale2, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
heatmap = heatmap1 + heatmap2
# apply merging the heatmap with multiple frames, 5 frames
heatmaps = deque(maxlen=5)
heatmaps.append(heatmap)
combined = sum(heatmaps)
heatmap = apply_threshold(combined, 2)
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(img), labels)
return draw_img
process video pipeline¶
In [19]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import imageio
imageio.plugins.ffmpeg.download()
vehicle_detect_output = './output_project_video.mp4'
clip1 = VideoFileClip("./project_video.mp4")
vehicle_detect_clip = clip1.fl_image(pipeline) #NOTE: this function expects color images!!
%time vehicle_detect_clip.write_videofile(vehicle_detect_output, audio=False)
In [15]:
# Showing the last frame of the output video frame
cap = cv2.VideoCapture('./output_project_video.mp4')
length = cap.get(cv2.CAP_PROP_FRAME_COUNT)
print(length)
cap.set(cv2.CAP_PROP_POS_FRAMES, length - 1)
ret, last_frame = cap.read()
plt.imshow(cv2.cvtColor(last_frame, cv2.COLOR_BGR2RGB))
# plt.imshow(last_frame)
Out[15]:
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